Using Discrete Choice Experiments to Derive Individual-Specific WTP Estimates for Landscape Improvements under Agri-Environmental Schemes: Evidence from the Rural Environment Protection Scheme in Ireland

Reported in this paper are the findings from two discrete choice experiments that were carried out to address the value of a number of farm landscape improvement measures within the Rural Environment Protection (REP) Scheme in Ireland. Image manipulation software is used to prepare photorealistic simulations representing the landscape attributes across three levels to accurately represent what is achievable within the Scheme. Using a mixed logit specification willingness to pay (WTP) distributions based on the parameter estimates obtained from the individual conditional distributions are derived. These estimates are subsequently adjusted and combined to account for baselines and levels of improvement resulting from the implementation of the REP Scheme. Individual-specific WTP estimates are thus obtained for the contribution of the Scheme to rural landscapes and are subsequently contrasted with the average cost of the Scheme across the Irish adult population. Results indicate that the Scheme contributes substantial benefits to rural landscapes.Agri-environment, Discrete choice experiments, Individual-specific WTP, Mixed logit

Financing Constraints and a Firm's Decision and Ability to Innovate: Establishing Direct and Reverse Effects.

The paper analyzes the existence and impact of financing constraints as a possibly serious obstacle to innovation by .rms. The econometric framework we employ in our study is the simultaneous bivariate probit with mutual endogeneity of direct indicators of financial constraints and innovation decisions by firms. A novel method for establishing coherency conditions is used. It allows us for the first time to estimate models hitherto classified as incoherent through the use of prior sign restrictions on model parameters. We are thus able to quantify the interaction between financing constraints and a firm's decision and ability to innovate without forcing the econometric models to be recursive. Hence, we obtain direct as well as reverse interaction effects, leading us to conclude that binding financing constraints discourage innovation and at the same time innovative firms are more likely to face binding financing constraints.DSGE model ; Currency union ; Heterogeneity ; Matching frictions ; Welfare.

Accelerating the Information-Theoretic Approach of Community Detection Using Distributed and Hybrid Memory Parallel Schemes

There are several approaches for discovering communities in a network (graph). Despite being approximating in nature, discovering communities based on the laws of Information Theory has a proven standard of accuracy. The information-theoretic algorithm known as Infomap developed a decade ago for detecting communities, did not foresee the tremendous growth of social networking, multimedia, and massive information boom. To discover communities in massive networks, we have designed a distributed-memory-parallel Infomap in the MPI framework. Our design reaches scalability of over 500 processes capable of processing networks with millions of edges while maintaining quality comparable to the sequential Infomap. We have further developed a novel parallel hybrid approach for Infomap consists of both distributed and shared memory parallelism using MPI and OpenMP frameworks. This achieves a speedup of more than 11x in processing a network of over 100 million edges which is significantly greater than the state-of-the-art techniques

Combining mixed logit models and random effects models to identify the determinants of willingness to pay for rural landscape improvements

This paper reports the findings from a discrete choice experiment study designed to estimate the economic benefits associated with rural landscape improvements in Ireland. Using a mixed logit model, the panel nature of the dataset is exploited to retrieve willingness to pay values for every individual in the sample. This departs from customary approaches in which the willingness to pay estimates are normally expressed as measures of central tendency of an a priori distribution. In a different vein from analysis conducted in previous discrete choice experiment studies, this paper uses random effects models for panel data to identify the determinants of the individual-specific willingness to pay estimates. In comparison with the standard methods used to incorporate individual-specific variables into the analysis of discrete choice experiments, the analytical approach outlined in this paper is shown to add considerably more validity and explanatory power to welfare estimatesAgri-environment, discrete choice experiments, mixed logit, panel data, random effects, willingness to pay, Demand and Price Analysis, Environmental Economics and Policy, C33, C35, Q24, Q51,

A survey of statistical network models

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference

Machine learning for modelling urban dynamics

We live in the age of cities. More than half of the world’s population live in cities and this urbanisation trend is only forecasted to continue. To understand cities now and in the foreseeable future, we need to take seriously the idea that it is not enough to study cities as sets of locations as we have done in the past. Instead, we need to switch our traditional focus from locations to interactions and in doing so, invoke novel approaches to modelling cities. Cities are becoming “smart” recording their daily interactions via various sensors and yielding up their secrets in large databases. We are faced with an unprecedented opportunity to reason about them directly from such secondary data. In this thesis, we propose model-based machine learning as a flexible framework for reasoning about cities at micro and macro scales. We use model-based machine learning to encode our knowledge about cities and then to automatically learn about them from urban tracking data. Driven by questions about urban dynamics, we develop novel Bayesian inference algorithms that improve our ability to learn from highly complex, temporal data feeds, such as tracks of vehicles in cities. Overall, the thesis proposes a novel machine learning toolkit, which, when applied to urban data, can challenge how we can think about cities now and about how to make them ”smarter”